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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H
namespace Eigen {
/** \class TensorConcatenationOp
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor concatenation class.
*
*
*/
namespace internal {
template<typename Axis, typename LhsXprType, typename RhsXprType>
struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename promote_storage_type<typename LhsXprType::Scalar,
typename RhsXprType::Scalar>::ret Scalar;
typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
typename traits<RhsXprType>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<LhsXprType>::Index,
typename traits<RhsXprType>::Index>::type Index;
typedef typename LhsXprType::Nested LhsNested;
typedef typename RhsXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
static const int NumDimensions = traits<LhsXprType>::NumDimensions;
static const int Layout = traits<LhsXprType>::Layout;
enum { Flags = 0 };
};
template<typename Axis, typename LhsXprType, typename RhsXprType>
struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense>
{
typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
};
template<typename Axis, typename LhsXprType, typename RhsXprType>
struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type>
{
typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
};
} // end namespace internal
template<typename Axis, typename LhsXprType, typename RhsXprType>
class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors>
{
public:
typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
typedef typename internal::traits<TensorConcatenationOp>::Index Index;
typedef typename internal::nested<TensorConcatenationOp>::type Nested;
typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
: m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename LhsXprType::Nested>::type&
lhsExpression() const { return m_lhs_xpr; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename RhsXprType::Nested>::type&
rhsExpression() const { return m_rhs_xpr; }
EIGEN_DEVICE_FUNC const Axis& axis() const { return m_axis; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorConcatenationOp& operator = (const TensorConcatenationOp& other)
{
typedef TensorAssignOp<TensorConcatenationOp, const TensorConcatenationOp> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorConcatenationOp& operator = (const OtherDerived& other)
{
typedef TensorAssignOp<TensorConcatenationOp, const OtherDerived> Assign;
Assign assign(*this, other);
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
protected:
typename LhsXprType::Nested m_lhs_xpr;
typename RhsXprType::Nested m_rhs_xpr;
const Axis m_axis;
};
// Eval as rvalue
template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
{
typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
typedef typename XprType::Index Index;
static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || NumDims == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((NumDims == RightNumDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(0 <= m_axis && m_axis < NumDims);
const Dimensions& lhs_dims = m_leftImpl.dimensions();
const Dimensions& rhs_dims = m_rightImpl.dimensions();
{
int i = 0;
for (; i < m_axis; ++i) {
eigen_assert(lhs_dims[i] > 0);
eigen_assert(lhs_dims[i] == rhs_dims[i]);
m_dimensions[i] = lhs_dims[i];
}
eigen_assert(lhs_dims[i] > 0); // Now i == m_axis.
eigen_assert(rhs_dims[i] > 0);
m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
for (++i; i < NumDims; ++i) {
eigen_assert(lhs_dims[i] > 0);
eigen_assert(lhs_dims[i] == rhs_dims[i]);
m_dimensions[i] = lhs_dims[i];
}
}
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_leftStrides[0] = 1;
m_rightStrides[0] = 1;
m_outputStrides[0] = 1;
for (int j = 1; j < NumDims; ++j) {
m_leftStrides[j] = m_leftStrides[j-1] * lhs_dims[j-1];
m_rightStrides[j] = m_rightStrides[j-1] * rhs_dims[j-1];
m_outputStrides[j] = m_outputStrides[j-1] * m_dimensions[j-1];
}
} else {
m_leftStrides[NumDims - 1] = 1;
m_rightStrides[NumDims - 1] = 1;
m_outputStrides[NumDims - 1] = 1;
for (int j = NumDims - 2; j >= 0; --j) {
m_leftStrides[j] = m_leftStrides[j+1] * lhs_dims[j+1];
m_rightStrides[j] = m_rightStrides[j+1] * rhs_dims[j+1];
m_outputStrides[j] = m_outputStrides[j+1] * m_dimensions[j+1];
}
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
// TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear?
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/)
{
m_leftImpl.evalSubExprsIfNeeded(NULL);
m_rightImpl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
{
m_leftImpl.cleanup();
m_rightImpl.cleanup();
}
// TODO(phli): attempt to speed this up. The integer divisions and modulo are slow.
// See CL/76180724 comments for more ideas.
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
// Collect dimension-wise indices (subs).
array<Index, NumDims> subs;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 1; i > 0; --i) {
subs[i] = index / m_outputStrides[i];
index -= subs[i] * m_outputStrides[i];
}
subs[0] = index;
} else {
for (int i = 0; i < NumDims - 1; ++i) {
subs[i] = index / m_outputStrides[i];
index -= subs[i] * m_outputStrides[i];
}
subs[NumDims - 1] = index;
}
const Dimensions& left_dims = m_leftImpl.dimensions();
if (subs[m_axis] < left_dims[m_axis]) {
Index left_index;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
left_index = subs[0];
for (int i = 1; i < NumDims; ++i) {
left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
}
} else {
left_index = subs[NumDims - 1];
for (int i = NumDims - 2; i >= 0; --i) {
left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
}
}
return m_leftImpl.coeff(left_index);
} else {
subs[m_axis] -= left_dims[m_axis];
const Dimensions& right_dims = m_rightImpl.dimensions();
Index right_index;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
right_index = subs[0];
for (int i = 1; i < NumDims; ++i) {
right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
}
} else {
right_index = subs[NumDims - 1];
for (int i = NumDims - 2; i >= 0; --i) {
right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
}
}
return m_rightImpl.coeff(right_index);
}
}
// TODO(phli): Add a real vectorization.
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index + packetSize - 1 < dimensions().TotalSize());
EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
for (int i = 0; i < packetSize; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
2 * TensorOpCost::MulCost<Index>() +
TensorOpCost::DivCost<Index>() +
TensorOpCost::ModCost<Index>());
const double lhs_size = m_leftImpl.dimensions().TotalSize();
const double rhs_size = m_rightImpl.dimensions().TotalSize();
return (lhs_size / (lhs_size + rhs_size)) *
m_leftImpl.costPerCoeff(vectorized) +
(rhs_size / (lhs_size + rhs_size)) *
m_rightImpl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, compute_cost);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims> m_leftStrides;
array<Index, NumDims> m_rightStrides;
TensorEvaluator<LeftArgType, Device> m_leftImpl;
TensorEvaluator<RightArgType, Device> m_rightImpl;
const Axis m_axis;
};
// Eval as lvalue
template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
: public TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
{
typedef TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device> Base;
typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
typedef typename Base::Dimensions Dimensions;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(XprType& op, const Device& device)
: Base(op, device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE);
}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
{
// Collect dimension-wise indices (subs).
array<Index, Base::NumDims> subs;
for (int i = Base::NumDims - 1; i > 0; --i) {
subs[i] = index / this->m_outputStrides[i];
index -= subs[i] * this->m_outputStrides[i];
}
subs[0] = index;
const Dimensions& left_dims = this->m_leftImpl.dimensions();
if (subs[this->m_axis] < left_dims[this->m_axis]) {
Index left_index = subs[0];
for (int i = 1; i < Base::NumDims; ++i) {
left_index += (subs[i] % left_dims[i]) * this->m_leftStrides[i];
}
return this->m_leftImpl.coeffRef(left_index);
} else {
subs[this->m_axis] -= left_dims[this->m_axis];
const Dimensions& right_dims = this->m_rightImpl.dimensions();
Index right_index = subs[0];
for (int i = 1; i < Base::NumDims; ++i) {
right_index += (subs[i] % right_dims[i]) * this->m_rightStrides[i];
}
return this->m_rightImpl.coeffRef(right_index);
}
}
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x)
{
const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_STATIC_ASSERT((packetSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index + packetSize - 1 < this->dimensions().TotalSize());
EIGEN_ALIGN_MAX CoeffReturnType values[packetSize];
internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
for (int i = 0; i < packetSize; ++i) {
coeffRef(index+i) = values[i];
}
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H